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Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β^sub 1^ receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H^sub 4^ receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.
The creation of target-specific 'magic bullets' has been a therapeutic goal since Ehrlich1, and a pragmatic criterion in drug designfor 30 years. Still, several lines of evidence suggest that drugs may havemany physiological targets2-5. Psychiatric medications, for instance, notoriously act through multiple molecular targets, and this 'polypharmacology' is probably therapeutically essential6. Recent kinase drugs, such as Gleevec and Sutent, although perhaps designed for specificity, modulate several targets, and these 'off-target' activities may also be essential for efficacy7,8. Conversely, anti-Parkinsonian drugs such as Permax and Dostinex activate not only dopamine receptors but also 5-HT2B serotonin receptors, thereby causing valvular heart disease and severely restricting their use9.
Predicting drug polypharmacology
Drug polypharmacology has inspired efforts to predict and characterize drug-target associations10-15. Several groups have used phenotypic and chemical similarities among molecules to identify those with multiple targets16,17, and early drug candidates are screened against molecular target panels18. To predict new targets for established drugs, a previous group looked for side-effects shared between two molecules19, whereas another group linked targets by drugs that bind to more than one of them20. Indeed, using easily accessible associations, one can map 332 targets by the 290 drugs that bind to at least two of them, resulting in a network with 972 connections (Fig. 1a). It seemed interesting to...